Hi Alex
This error is likely due to the Qdec variable being empty. So, nothing was read
into this variable when you applied
Qdec = fReadQdec('qdec.table.dat');
Please check that. If you don't find a solution to this then send me your Qdec
table data file and I will check it out.
Best
-Jorge
>________________________________
> De: Alex Hanganu <al.hang...@yahoo.ca>
>Para: jorge luis <jbernal0...@yahoo.es>
>CC: FS Mailing List <Freesurfer@nmr.mgh.harvard.edu>
>Enviado: Miércoles 5 de diciembre de 2012 13:50
>Asunto: Re: [Freesurfer] Longitudinal analysis - contrast
>
>
>Hi Jorge,
>
>thanks for such a detailed help !!!
>
>Martin explained very well the advantages of this method, so we
are working on it now, and we have an Error.
>
>we loaded the lh.thickness_sm10.mgh file, read the surface and
label. Then we read the qdec.table.dat.
>During the "sids = Qdec(:,1);" cmd we have an error:
>"Index exceeds matrix dimensions"
>
>does this mean we have an error in the qdec or fsgd file ? These
files seems to be correct.
>
>Thanks !
>
>Sincerely,
>
>Alex.
>
>Le 04/12/2012 8:45 PM, jorge luis a écrit :
>
>Hi Alex
>>
>>I think that your design is very simple and so will be to use our lme tools.
>>There is an example of a mass-univariate analysis on the wiki at
>>http://surfer.nmr.mgh.harvard.edu/fswiki/LongitudinalStatistics. However, the
>>analysis there is much more complex (because of the more complex data) than
>>what you actually need.
>> Since you only have two repeated measures your model only requires one
>>random effect for the intercept term (a compound symmetry covariance always
>>hold when there are only two repeated measures). So you don’t have to carry
>>out any model selection procedure. Your design matrix only contains four
>>colums
>>1- A column of 1s (intercept term)
>>2- The time covariate (or alternative a variable composed of 0s and 1s
>>indicating whether the measurement occasion is the second time point or not).
>>3- The group covariate (composed of 0s and 1s indicating group membership)
>>4- The group by time interaction (the point to point product of the previous
>>second and third covariates).
>> Once you have run the Freesurfer’s mris_preproc and mri_surf2surf cmd it is
>>quite easy to read the generated data into Matlab using lme. If you think
>>that it is to difficult to apply the spatiotemporal mixed-effects model at
>>least try the voxel-wise mixed model. It only requires you to run one Matlab
>>function
>> lme_mass_fit_vw(X,1,Y,ni,cortex)
>>were X is your ordered design matrix, Y is the cortical thickness data
>>matrix, ni is 2*ones(36,1) and cortex is your average subject's cortex label.
>>
>>Even that voxel-wise mixed model analysis will perform much better than other
>>approaches (both in power and family wise error). Then you only need to test
>>if the coefficient for the interaction term is greater than zero with the
>>contrast C=[0 0 0 1] and write your results for visualization in Freesurfer
>>(or you can obtained the anatomical coordinates of the detected clusters usin
>>the mri_surfcluster cmd).
>>I think that what is called “RepeatedMeasuresAnova” in the wiki is not
>>actually a true repeated measures Anova analysis and in your case (two
>>repeated measures and two groups) it will give you exactly the same results
>>as using the mris_slope cmd. The only advantage of those methods over the
>>lme tool at the moment is the possibility of using other multiple comparison
>>methods in addition to FDR.
>>I encourage people to give a try to the lme tools because it will pay good
>>dividends after all, especially for those studies with more than three
>>repeated measures.
>>Just write to me any doubt you have and I will answer you ASAP.
>>Best
>>-Jorge
>>
>>
>>De: Alex Hanganu <al.hang...@yahoo.ca>
>>>Para: Martin Reuter <mreu...@nmr.mgh.harvard.edu>
>>>CC: FS Mailing List <Freesurfer@nmr.mgh.harvard.edu>
>>>Enviado: Martes 4 de diciembre de 2012 19:19
>>>Asunto: Re: [Freesurfer] Longitudinal analysis - contrast
>>>
>>>Hello Martin,
>>>
>>>thanks for the quick reply !
>>>
>>>as I understood, from the "LongitudinalTutorial", after
long_mris_slopes
>>>the results can be seen for each subject, and in order
to see the group
>>>results, all subjects should be analysed in the Qdec,
and there I could
>>>see the results for each group, but couldn't do the
inter-group, this is
>>>why we tried the "RepeatedMeasuresAnova"
>>>
>>>Indeed, there is some difference in the time distance,
so we will try
>>>your new idea !
>>>
>>>I know about Jorge's work - Marvellous ! but it seems to
need too much
>>>time to be comprehended and applied. We hoped to be done
with these
>>>results and start already writing the paper :)
>>>
>>>Thank you very much for Your help !!!
>>>
>>>Sincerely,
>>>Alex.
>>>
>>>
>>>
>>>
>>>Le 04/12/2012 6:44 PM, Martin Reuter a écrit :
>>>> Hi Alex,
>>>>
>>>> I am not familiar with the way Doug describes the
repeated measure anova
>>>> on the wiki. Of course in a longitudinal setting
repeated measures are
>>>> correlated and I am not sure if this is considered
in that model there.
>>>>
>>>> Since you have only 2 time points, why not simply
compare the difference
>>>> (or weighted by the time distance, if the time
points are not the same
>>>> distance apart)?
>>>>
>>>> You would compute (tp2-tp1)/time for each subject
and then compare this
>>>> across groups with a standard glm.
>>>> Since in the longitudinal stream both thickness
maps are registered, you
>>>> can simply use mris_calc to compute the difference
directly.
>>>>
>>>> There are also scripts for this (long_mris_slopes,
even for more than 2
>>>> time points, where we fit a line into each
subject). See the
>>>> http://surfer.nmr.mgh.harvard.edu/fswiki/FsTutorial/LongitudinalTutorial
>>>> This is a simple approach, first reducing the
variable of interest
>>>> (change across time) to a single number per subject
and then running a
>>>> standard test. It should be sufficient for your
setting.
>>>>
>>>> You can also do more complex modeling using our new
linear mixed effects
>>>> models if you want (see older email from Jorge
about that). It considers
>>>> both temporal and spacial correlation of measures.
This model is
>>>> especially useful if you have differently many time
points and time
>>>> distances per subject.
>>>>
>>>> Best, Martin
>>>>
>>>>
>>>> On Tue, 2012-12-04 at 18:26 -0500, Alex Hanganu
wrote:
>>>>> Dear Freesurfer Experts,
>>>>>
>>>>> We are analysing longitudinal data - the
difference between 2 groups (P
>>>>> and M) [19 and 17 subjects] with 2 time points
for each group (A, B).
>>>>>
>>>>> We are using the example of "Repeated Measures
Anova"
>>>>> (http://surfer.nmr.mgh.harvard.edu/fswiki/RepeatedMeasuresAnova)
>>>>>
>>>>> For our first approach - we took all the
subjects - 36 classes, and
>>>>> tried to create the contrast for:
>>>>>
>>>>> P(B-A) - M(B-A)
>>>>> 2 within subject factors, and 2 inter-subject
>>>>>
>>>>> We considered the recent explanations
>>>>> (http://www.mail-archive.com/freesurfer@nmr.mgh.harvard.edu/msg25459.html),
>>>>> and, as we understood, our null hypothesis is:
>>>>>
>>>>> PB-PA=0 AND MB-MA=0 ->
>>>>> Combining: PB-PA-MB+MA=0
>>>>>
>>>>> and the matrix seems to be:
>>>>> 0 0 0 .... (36 zeros) -1
>>>>> 0 0 0 .... (36 zeros) 1
>>>>> 0 0 0 .... (36 zeros) 1
>>>>> 0 0 0 .... (36 zeros) -1
>>>>>
>>>>> but we still get a dimension mismatch between X
and C: X has 72, C has 37.
>>>>>
>>>>> The fsgd file is like this:
>>>>> Class subject 1
>>>>> .
>>>>> .
>>>>> .
>>>>> Class subject 36
>>>>> Variables TP1-vs-TP2
>>>>> Input groupPsubj1-A Subject1 -1
>>>>> Input groupPsubj1- B Subject1 1
>>>>> Input groupPsubj2-A Subject2 -1
>>>>> .
>>>>> .
>>>>> .
>>>>> Input groupMsubj35-A Subject35 1
>>>>> Input groupMsubj35-B Subject35 -1
>>>>> Input groupMsubj36-A Subject36 1
>>>>> Input groupMsubj36-B Subject36 -1
>>>>>
>>>>> ==============
>>>>>
>>>>> Our second approach - we run mris_glmfit for
each group separately and
>>>>> then we wanted to use mris_calc to compute the
difference:
>>>>>
>>>>> mris_calc -o avg.mgh
groupP/glm-dir/Contrast/sig.mgh add
>>>>> groupM/glm-dir/Contrast/sig.mgh
>>>>>
>>>>> though the sig.mgh for each group shows
significant results, the avg.mgh
>>>>> reveals no effect.
>>>>>
>>>>> Can you please help with this analysis ?
>>>>>
>>>>> Thanks!
>>>>>
>>>>> Sincerely,
>>>>> Alex.
>>>>> ____________________
>>>
>>>
>
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